Sai Guna Ranjan Gurazada, MS, University of Delaware Dr. Shawn Polson, PhD, University of Delaware
Poster # 31
The large-scale production of monoclonal antibodies (mAbs) has been an important focus for the biopharmaceutical industry, with mammalian cell cultures, particularly the Chinese hamster ovarian (CHO) cells being the preferred host cell factories for their manufacture. Persistent improvements in bioprocess conditions, culture conditions and CHO cell line development have led to significant increase in mAb productivity, with titers increasing from 3-10 g/L over the past two decades. However, the molecular mechanisms of the host cell that have led to these improvements are not well understood. Application of omics technologies (e.g. genomics, transcriptomics, epigenomics, proteomics or metabolomics) individually has revealed valuable insights. However, a multi-omics systems biology approach, that combines different omics data has greater potential in piecing together the complex biological layers and drawing a more complete picture of the cellular mechanisms involved in mAb production. This understanding will enable rational engineering strategies that can potentially deliver even higher yields and quality of mAbs being produced in CHO cell cultures. Here, we present our efforts in implementing a machine learning-based computational framework for the integration and analysis of multi-omics data with the primary goal of answering two questions: a) Can multimodal analysis of the CHO cell system using omics data accurately predict productivity levels during monoclonal antibody (mAb) manufacturing, and b) If yes, what are the biological markers correlating to desirable productivity traits. The multi-omics framework that we establish can be generalized and have broader applications to other mammalian or non-mammalian cellular systems including HEK293 (used to produce viral vectors for gene therapy), plants and even bacteria.
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